Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/594
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dc.contributor.authorGhoreishi, B.en_US
dc.contributor.authorKhaleghi Esfahani, M.en_US
dc.contributor.authorAlizadeh Lushabi, N.en_US
dc.contributor.authorAmini, O.en_US
dc.contributor.authorAghamolaie, I.en_US
dc.contributor.authorHashim, N.A.A.N.en_US
dc.contributor.authorAlizadeh, S.M.S.en_US
dc.date.accessioned2021-01-25T05:30:05Z-
dc.date.available2021-01-25T05:30:05Z-
dc.date.issued2021-01-
dc.identifier.issn09603182-
dc.identifier.urihttp://hdl.handle.net/123456789/594-
dc.descriptionWeb of Science / Scopusen_US
dc.description.abstractIn this research, geotechnical properties and the relationship between cohesion (c) and internal friction angle (phi) with the SPT-N(60)were investigated in 120 boreholes in the sedimentary basin of Kerman. Laboratory tests such as direct shear, triaxial, consolidation, and physical tests were carried out on soil samples extracted from the boreholes, and the SPT test was performed on all 120 boreholes. Since the soil in the area is CL, the SEM, XRD, XRF, physical, and mechanical properties of this soil were investigated. The artificial neural networks (ANN) and statistical analysis were used to estimate phi and c based on the SPT-N-60. The petrography studies revealed that Quartz, Calcite, Dolomite, Albite, Illite, Clinochlore, and Microcline are the most plentiful minerals in this sedimentary basin. Also, the dominant clay is Illite. Illite clays, due to the low shear strength, have made some problems in the earth dams of the studied area. Results show that based on the SPT-N number, groundwater level, and soil texture the liquefaction hazard could not occur in this area. Previous equations are used to predict the c and phi and results are compared with this research. The obtained results from the ANN and statistical analysis showed that there is a good correlation between phi and c derived from the direct shear test with the SPT-N-60. Based onR(2), RMSE,P-value and Durbin-Watson statistics the correlation between c and the SPT-N(60)is stronger than phi and the SPT-N-60. Moreover, the ANN showed higher accuracy in predicting shear strength parameters compared to the simple regression.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofGeotechnical and Geological Engineeringen_US
dc.subjectArtificial neural networksen_US
dc.subjectGeotechnical propertiesen_US
dc.subjectKerman sedimentary basinen_US
dc.subjectShear strength parametersen_US
dc.subjectStandard penetration test (SPT)en_US
dc.titleAssessment of Geotechnical Properties and Determination of Shear Strength Parametersen_US
dc.typeInternationalen_US
dc.identifier.doi10.1007/s10706-020-01504-1-
dc.description.page461-478en_US
dc.volume39 (1)en_US
dc.description.typeArticleen_US
item.languageiso639-1en-
item.openairetypeInternational-
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:Faculty of Hospitality, Tourism and Wellness - Journal (Scopus/WOS)
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